We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Papers
61,005 resultsShowing papers similar to Machine Learning Microplastic Characterisation Surpasses Human Performance and Uncovers Labelling Errors in Public FTIR Data
ClearRobust Automatic Identification of Microplastics in Environmental Samples Using FTIR Microscopy
Researchers developed a robust automated method for identifying microplastics in environmental samples using FTIR microscopy combined with machine learning-based spectral matching, improving the consistency and efficiency of microplastic identification compared to manual evaluation.
Machine learning outperforms humans in microplastic characterization and reveals human labelling errors in FTIR data
Researchers developed a small but powerful neural network that can identify microplastic types from infrared spectroscopy data more accurately than human experts. The AI model classified 16 different categories of microplastics and even revealed errors in human-labeled data. This technology could dramatically speed up microplastic analysis in environmental and health studies, making it easier to understand the scale and types of microplastic contamination people are exposed to.
Computer-Assisted Analysis of Microplastics in Environmental Samples Based on μFTIR Imaging in Combination with Machine Learning
Researchers developed machine learning approaches for automated microplastic identification in environmental samples from micro-FTIR imaging data, demonstrating improved accuracy and speed compared to traditional spectral library search methods for scalable analysis.
A Comparative Study of Machine Learning and Deep Learning Models for Microplastic Classification using FTIR Spectra
Researchers compared machine learning and deep learning models for classifying microplastics using FTIR spectra, evaluating multiple algorithmic approaches against standardised spectral datasets. The study assessed classification accuracy and computational efficiency, identifying which model architectures best discriminate between polymer types in environmental microplastic samples.
An ensemble machine learning method for microplastics identification with FTIR spectrum
Researchers developed an ensemble machine learning method to automatically identify microplastics using Fourier transform infrared (FTIR) spectroscopy data. The approach combines multiple classification algorithms to improve accuracy over individual methods for detecting and categorizing microplastic particles. The study suggests this automated approach could help standardize and accelerate microplastic monitoring in marine environments.
FTIR-Based Microplastic Classification: A Comprehensive Study on Normalization and ML Techniques
Researchers tested machine learning and deep learning techniques for classifying six common types of microplastics using infrared spectroscopy data. They found that using broader spectral ranges and certain normalization techniques significantly improved classification accuracy. The study demonstrates that automated identification of microplastic types is feasible and could speed up environmental monitoring efforts.
PlasticNet: Deep Learning for Automatic Microplastic Recognition via FT-IR Spectroscopy
Researchers developed PlasticNet, a deep learning algorithm that automatically identifies microplastic types from infrared spectral data, outperforming conventional library matching approaches. Automating microplastic identification could dramatically speed up the analysis of environmental samples and reduce human error.
A machine learning algorithm for high throughput identification of FTIR spectra: Application on microplastics collected in the Mediterranean Sea
Researchers developed a machine learning method to automatically identify the chemical composition of microplastics from FTIR spectroscopy data collected during the Tara Mediterranean expedition. The algorithm performed well for common polymers like polyethylene and was applied to classify over 4,000 unidentified microplastic spectra. The study demonstrates that automated identification tools can significantly speed up large-scale microplastic pollution surveys while maintaining acceptable accuracy.
The Identification and Classification of Microplastics by FTIR Using Gaussian Mixture and Naive Bayes
Researchers developed a machine learning approach using Gaussian Mixture models and Naive Bayes classification to automate the identification and classification of microplastics from FTIR spectral data, addressing the challenge of variable-length spectral outputs. The method successfully standardized data preprocessing to equal-length inputs and achieved high classification accuracy, offering a tool to support and accelerate manual polymer identification.
Reference database design for the automated analysis of microplastic samples based on Fourier transform infrared (FTIR) spectroscopy
A reference database for automated FTIR-based microplastic identification was developed using hierarchical cluster analysis of reference spectra, enabling both single-particle identification and chemical imaging analysis. The database design improves the reproducibility and comparability of automated microplastic identification across different laboratories and instrumentation types.
Automated Machine-Learning-Driven Analysis of Microplastics by TGA-FTIR for Enhanced Identification and Quantification
Researchers developed an automated machine-learning system to identify and measure microplastics using a combination of heat analysis and infrared spectroscopy. The system can distinguish between different plastic types more accurately and faster than manual methods. Better detection tools like this are important because reliable measurement of microplastics in food, water, and the environment is essential for understanding human exposure levels.
Spectral Classification of Large-Scale Blended (Micro)Plastics Using FT-IR Raw Spectra and Image-Based Machine Learning
Researchers developed and compared four machine learning classifiers for identifying microplastic types from Fourier transform infrared spectroscopy data using large-scale blended plastic datasets. The study found that a 1D convolutional neural network achieved the best overall accuracy at over 97%, outperforming decision tree and random forest models, offering a scalable alternative to traditional library-search methods for microplastic identification.
Automated Machine-Learning-DrivenAnalysis of Microplasticsby TGA-FTIR for Enhanced Identification and Quantification
Researchers developed an automated machine-learning-driven analysis pipeline for characterizing microplastics using thermogravimetric analysis coupled with FTIR, achieving rapid polymer identification and quantification that could enable high-throughput environmental monitoring.
Optimized recognition of microplastic ATR-FTIR spectra with deep learning
Researchers developed an optimized deep learning method for identifying microplastics from ATR-FTIR spectra, improving classification accuracy for weathered and environmentally contaminated MP samples that challenge standard spectral library matching approaches.
Automated Classification of Undegraded and Aged Polyethylene Terephthalate Microplastics from ATR-FTIR Spectroscopy using Machine Learning Algorithms
Researchers applied seven machine learning algorithms to ATR-FTIR spectroscopy data for automated classification of undegraded and aged polyethylene terephthalate (PET) microplastics, with Random Forest, Gradient Boosting, Decision Tree, and k-Nearest Neighbors all achieving 99% classification accuracy. The approach leverages distinct chemical signatures in FTIR spectra to distinguish degradation states, offering a scalable alternative to labor-intensive manual microplastic analysis.
Automatic microplastic classification using dual-modality spectral and image data for enhanced accuracy
A dual-modality classification system combining FTIR spectral data and microscope images achieved 99% accuracy in automatically identifying five common microplastic polymer types. The study deployed a web application (MPsSpecClassify) that enables researchers to efficiently classify microplastics, addressing the time-consuming and error-prone nature of manual spectral analysis.
Know What You Don’t Know: Assessment of Overlooked Microplastic Particles in FTIR Images
A reference image dataset containing over 1,200 microplastic and non-microplastic particles was developed to evaluate whether FTIR-based data analysis routines miss any particles during automated microplastic identification. Many existing routines overlooked a significant fraction of particles, particularly smaller ones. Better evaluation tools are needed to ensure that automated microplastic analysis is complete and accurate.
Comparison of two rapid automated analysis tools for large FTIR microplastic datasets
Researchers compared two automated analysis tools for large FTIR microplastic datasets and found significant differences in polymer identification results, highlighting the urgent need for standardized data analysis methods in microplastic research.
Deep Learning for Reconstructing Low-Quality FTIR and Raman Spectra─A Case Study in Microplastic Analyses
Researchers developed a deep learning method to reconstruct low-quality FTIR and Raman spectra, demonstrating its effectiveness for automated microplastic analysis where rapid measurement workflows produce noisy, challenging spectral datasets.
Functional Group Identification for FTIR Spectra Using Image-Based Machine Learning Models
Researchers developed a machine learning model that uses images of FTIR spectra to automatically identify chemical functional groups in unknown substances. This approach could speed up the identification of microplastic polymer types in environmental samples, making large-scale monitoring more efficient.
Machine Learning Method for Microplastic Identification Using a Combination of Machine Learning and Raman Spectroscopy
Researchers developed a machine learning method for identifying microplastics using a combination of multiple spectroscopic techniques, improving classification accuracy beyond single-method approaches and enabling automated polymer identification.
Generation of synthetic FTIR spectra to facilitate chemical identification of microplastics
Researchers generated synthetic FTIR spectra of microplastics using computational methods to augment training datasets for automated spectral identification algorithms. The synthetic spectra closely matched experimentally measured spectra, and classifiers trained on augmented datasets showed improved accuracy for identifying underrepresented polymer types in real-world samples.
Machine learning-driven microplastics identification using ensemble stacking with Extra Tree meta-models from FTIR data
Researchers applied ensemble stacking machine learning to ATR-FTIR spectra for microplastic identification, finding that combining multiple classifier outputs improved polymer classification accuracy beyond any single model, particularly for weathered plastics with degraded spectral signatures.
Classifying polymers with mid-IR spectra and machine learning: From monitoring to detection
Researchers applied machine learning to mid-infrared spectra to automatically classify different types of plastic polymers found in the environment. Accurate polymer identification is essential for microplastic research, and this automated approach could improve monitoring efficiency and data consistency across studies.